A Novel and More Efficient Search Strategy of Quantum-Behaved Particle Swarm Optimization

  • Jun Sun
  • Choi H. Lai
  • Wenbo Xu
  • Zhilei Chai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4431)

Abstract

Based on the previous proposed Quantum-behaved Particle Swarm Optimization (QPSO), in this paper, a novel and more efficient search strategy with a selection operation is introduced into QPSO to improve the search ability of QPSO. While the center of position distribution of each particle in QPSO is determined by global best position and personal best position, in the Modified QPSO (MQPSO), the global best position is substituted by a personal best position of a randomly selected particle. The MQPSO also maintains the mean best position of the swarm as in the previous QPSO to make the swarm more efficient in global search. The experiment results on benchmark functions show that MQPSO has stronger global search ability than QPSO and PSO.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Angeline, P.J.: Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and performance Differences. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 601–610. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  2. 2.
    Angeline, P.J.: Using Selection to Improve Particle Swarm Optimization. In: Proc. 1998 IEEE International Conference on Evolutionary Computation, pp. 84–89 (1998)Google Scholar
  3. 3.
    Van den Bergh, F.: An Analysis of Particle Swarm Optimizers. PhD Thesis, University of Pretoria, South Africa (2001)Google Scholar
  4. 4.
    Clerc, M.: The Swarm and Queen: Towards a Deterministic and Adaptive Particle Swarm Optimization. In: Proc. 1999 Congress on Evolutionary Computation, pp. 1951–1957 (1999)Google Scholar
  5. 5.
    Clerc, M., Kennedy, J.: The Particle Swarm: Explosion, Stability, and Convergence in a Multi-dimensional Complex Space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002)CrossRefGoogle Scholar
  6. 6.
    Eberhart, R.C., Shi, Y.: Comparison between Genetic Algorithm and Particle Swarm Optimization. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 611–616. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  7. 7.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)Google Scholar
  8. 8.
    Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proc. IEEE 1995 International Conference on Neural Networks, IV, pp. 1942–1948 (1995)Google Scholar
  9. 9.
    Kennedy, J.: Small worlds and Mega-minds: Effects of Neighborhood Topology on Particle Swarm Performance. In: Proc. 1999 Congress on Evolutionary Computation, pp. 1931–1938 (1999)Google Scholar
  10. 10.
    Suganthan, P.N.: Particle Swarm Optimizer with Neighborhood Operator. In: Proc. 1999 Congress on Evolutionary Computation, pp. 1958–1962 (1999)Google Scholar
  11. 11.
    Sun, J., Feng, B., Xu, W.-B.: Particle Swarm Optimization with Particles Having Quantum Behavior. In: Proc. 2004 Congress on Evolutionary Computation, pp. 325–331 (2004)Google Scholar
  12. 12.
    Sun, J., Xu, W.-B., Feng, B.: A Global Search Strategy of Quantum-behaved Particle Swarm Optimization. In: Proc. 2004 IEEE Conference on Cybernetics and Intelligent Systems, Singapore, pp. 111–115 (2004)Google Scholar
  13. 13.
    Sun, J., Xu, W.-B., Feng, B.: Adaptive Parameter Control for Quantum-behaved Particle Swarm Optimization on Individual Level. In: Proc. 2005 IEEE International Conference on Systems, Man and Cybernetics, pp. 3049–3054 (2005)Google Scholar
  14. 14.
    Shi, Y., Eberhart, R.: Empirical Study of Particle Swarm Optimization. In: Proc. 1999 Congress on Evolutionary Computation, pp. 1945–1950 (1999)Google Scholar
  15. 15.
    Shi, Y., Eberhart, R.C.: A Modified Particle Swarm. In: Proc. 1998 IEEE International Conference on Evolutionary Computation, pp. 69–73 (1998)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Jun Sun
    • 1
  • Choi H. Lai
    • 2
  • Wenbo Xu
    • 1
  • Zhilei Chai
    • 1
  1. 1.Center of Intelligent and High Performance Computing, School of Information Technology, Southern Yangtze University, No. 1800, Lihudadao Road, Wuxi, 214122 JiangsuChina
  2. 2.School of Computing and Mathematical Sciences, University of Greenwich, Greenwich, London SE10 9LSUK

Personalised recommendations